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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

Traditional power system is facing challenges demanding new operational requirements to meet targets of Net Zero Emissions by 2050. Aggregators are playing progressively important role in the demand response (DR) electricity market but are often riddled with deep level of market monopoly and lack of transparency/secrecy. Emerging real-time information technology (IT) applications and novel modelling of digital twins (DT) of individual electricity assets are challenging this position by promising improvements and openness that allows TSO direct access to available demand side flexibility. Additionally, DT technologies are facilitating how demand response services are delivered to end-users by allowing individual assets participation at the atomic level. In this study, the application potentials of assets’ DT in participating in the demand response electricity market was examined. Again, an overview of DT applications for DR was conducted. The novelty of this study is highlighted in development of new approach that facilitates individual end-user assets’ contribution to demand response efforts. The research identifies key useful questions that might serve as inspiration for stakeholders and policy-makers to further close existing gaps in the field of DT, smart-grid and demand response. © 2023 European Union.

关键词： Aggregators demand response digital twins Industry 5.0 smart-grid

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Leveraging Semi-Supervised Learning for Early Diagnosis of Polycystic Ovary Syndrome (PCOS)

Leveraging Semi-Supervised Learning for Early Diagnosis of P...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Kaniz Fatema Tanni Mithila Mahmood Tanjina Alam M. Jamshed Alam Patwary Department of Computer Science & Engineering International Islamic University Chittagong Bangladesh Institute of Information & Communication Technology Chittagong University of Engineering & Technology

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder that can lead to serious health complications, including infertility, diabetes, and cardiovascular issues. Early detection is critical for effective management and treatment, but the challenge of limited labeled data persists. This study explores the use of Semi-Supervised Learning (SSL) techniques for PCOS detection, leveraging both labeled and unlabeled data to enhance the performance of machine learning models. Ten different machine learning classification algorithms, including Logistic Regression (LR), Decision Tree (DT), AdaBoost (AB), Random Forest (RF), and Support Vector Machine (SVM), were employed and compared in both SSL and Supervised Learning (SL) settings. The experimental results show that SSL models achieved accuracies ranging from 79.14% to 91.37%, which are comparable to, or even exceed, those of the SL models. Among the SSL models, AdaBoost and Random Forest achieved the highest accuracies of 91.37% and 89.21%, respectively. The SSL models also demonstrated competitive performance in terms of sensitivity, specificity, and AUC scores, proving their effectiveness in detecting PCOS with minimal labeled data. These findings highlight the potential of SSL as a promising alternative to traditional SL methods in PCOS diagnosis, offering a more efficient and reliable solution while reducing the reliance on large labeled datasets.

关键词： Support vector machines Accuracy Sensitivity Computational modeling Supervised learning Semisupervised learning Data models Distance measurement Random forests Testing

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EDITH: Enhanced Disease Identification and Tracking Through Hybridization

EDITH: Enhanced Disease Identification and Tracking Through ...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Soleti Navya Tuniki Sarika S. Parvathi P Chinnasamy Shaik Abdul Nabi Ajmeera Kiran Department of Computer Science and Information Technology MLR Institute of Technology Department of Information Technology CMR Engineering College Hyderabad CSE DEPT AVNIET JNTUH

The EDITH project represents a significant advance in the application of machine learning in healthcare. Harnessing the power of massive medical data and sophisticated algorithms, EDITH aims to transform disease detection and help design more accurate and personalized healthcare interventions. Ongoing research and development of the EDITH model will further enhance its capabilities and cement its position as an invaluable tool for medical diagnosis.

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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

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Mathematical Biosciences and engineering 2024年第4期21卷 5521-5535页

作者： Ran, Zhongnan Jiang, Mingfeng Li, Yang Wang, Zhefeng Wu, Yongquan Ke, Wei Xia, Ling School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Department of Cardiology Beijing Anzhen Hospital Capital Medical University No. 2 Anzhen Road Chaoyang District Beijing100029 China School of Applied Sciences Macao Polytechnic Institute China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively. © 2024 the Author(s).

关键词： Diseases

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A Review on Amalgamation of Textile-based Components for Standardization of Smart Fabrics

A Review on Amalgamation of Textile-based Components for Sta...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Sandeep Kumar Haripriya Neeti Taneja Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Computer Science Engineering Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India

This article provides an overview of the fundamental ideas and methodologies utilized in the construction of fabric sensors, along with the frequently employed materials and procedures in the field of electronic textiles. It illustrates that depending on the level of integration within the fabric platform, sensing capabilities may be accomplished by either intrinsic or external modifications to textile substrates. The present study showcases the adaptability of fabric sensors in quantifying force, pressure, chemicals, humidity, and temperature fluctuations. The materials, connections, fabric circuits, interconnect, encapsulation, and manufacturing processes used in fabric technologies are very adaptable and diverse. However, they are not as durable as the components used in traditional electronics. The survey results indicate that it is feasible to create a comprehensive smart fabric system by combining various types of functional elements based on textiles. This effort aims to initiate the standardization of smart fabric sensing techniques and e-textile production procedures.

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Improving Quality of Service Using a Innovative Energy Effective Reinforcement Based Path Finding in Mobile Ad Hoc Network 2

Improving Quality of Service Using a Innovative Energy Effec...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Singaravelan, M. Saleem Raja, A. Thangam, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai India College of Computing and information Sciences University of Technology and Applied Sciences Shinas Oman Amrita school of computing Department of computer science and engineering Bengaluru India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

RL stands for reinforcement learning, is a component of the broader field of machine learning. It provides a framework its features help to select an efficient action using previously learned information of interaction and its environment. Mobile nodes are grouped to form a network for communicating themselves termed autonomous network with mobile nodes (MANET). This is a dynamic and independent network requiring no administrative control. The mobile nodes in the network are to be part or apart at any time that causes the topology changes. The frequent topology changes make it hard to determine the destination route. The route discovery in the dynamic topology that consumes more node energy and, minimize the network lifetime. The energy usage and network lifetime are a vital part of the MANET. This paper proposes a new global routing protocol using a reinforcement learning algorithm called modified energy and signal strength Q-learning ad hoc on-demand distance vector algorithm (ESQAODV). This process conducts a result comparison to a local routing algorithm known as AODVSARSA. The proposed algorithm proved a better result with reference to average distribution ratio compared to the other, and complete delay metrics © 2024 IEEE.

关键词： Adversarial machine learning

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Blockchain-enabled cybersecurity for IoT using elliptic curve cryptography and black winged kite model

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International Journal of information technology (Singapore) 2025年 1-11页

作者： Pawar, Priyanka Pramod Femy, F. Fanax Rajkumar, N. Jeevitha, S. Bhuvanesh, A. Kumar, Deepak Department of Information Technology University of the Cumberlands Williamsburg United States Department of Computer Science Holy Cross College (Autonomous) Tamil Nadu Nagercoil India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Because of features like security, immutability, and decentralization, blockchain (BC) is important to cybersecurity. The development of Internet of Things (IoT) networks has created serious cybersecurity issues and necessitated the use of cutting-edge defenses against new dangers. Multi-Head Attention Bidirectional Long Short Term Memory (MHA-BiLSTM), a Deep Learning (DL) technique, is presented in this paper to improve cybersecurity in an Internet of Things setting. This work includes steps like data storage, encryption, decryption, and cyberattack detection. First, Elliptical Curve Cryptography (ECC) is used to encrypt the data, and Black-Winged Kite (BWK) optimization is used to optimize the ECC's key parameters. The data is stored in the BC following the encryption process. The data is then decrypted using ECC, and the MHA-Bi-LSTM completes the cybersecurity procedure. This model improves its ability to recognize and lessen cyberthreats. The proposed cybersecurity model significantly improved threat detection accuracy, according to analysis. This method offers a scalable, resilient, and cyberattack-proof model for securing IoT networks in real-time applications. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Blockchain Cyberattacks Cybersecurity Deep learning Enhanced black-winged kite

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DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Huang, Yunqi Gao, Dingchao Ying, Shenggang Li, Sanjiang Faculty of Engineering and Information Technology SydneyNSW2007 Australia Institute of Software Chinese Academy of Sciences Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science China

Neutral atom (NA) quantum systems are emerging as a leading platform for quantum computation, offering superior or competitive qubit count and gate fidelity compared to superconducting circuits and ion traps. However, the unique features of NA devices, such as long-range interactions, long qubit coherence time, and the ability to physically move qubits, present distinct challenges for quantum circuit compilation. In this paper, we introduce DasAtom, a novel divide-and-shuttle atom approach designed to optimise quantum circuit transformation for NA devices by leveraging these capabilities. DasAtom partitions circuits into subcircuits, each associated with a qubit mapping that allows all gates within the subcircuit to be directly executed. The algorithm then shuttles atoms to transition seamlessly from one mapping to the next, enhancing both execution efficiency and overall fidelity. For a 30-qubit Quantum Fourier Transform (QFT), DasAtom achieves a 415.8x improvement in fidelity over the move-based algorithm Enola and a 10.6x improvement over the SWAP-based algorithm Tetris. Notably, this improvement is expected to increase exponentially with the number of qubits, positioning DasAtom as a highly promising solution for scaling quantum computation on NA platforms. © 1982-2012 IEEE.

关键词： Qubits

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Performance Exploration of ML Algorithms in Security Facets of Cloud Computing

Performance Exploration of ML Algorithms in Security Facets ...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Kumud Saxena Siddarth Sharma Deepak Mehta Department of Computer Science Engineering Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

A computational worldview known as “cloud computing” gives clients the opportunity to scale and measure computer assets as well as the capacity to get to and utilize them at whatever point required. These days, a expansive rate of businesses depend intensely on computer innovation to realize a assortment of objectives, such as taken a toll lessening, foundation administration, improvement stages, information preparing, and information analytics. Web-based apps are advertised by cloud benefit suppliers (CSPs) to empower conclusion clients to get to administrations over the web, making it simpler for them to utilize these administrations at whatever point and wherever they need. The viability of machine learning strategies for cloud security is compared and evaluated in this investigate with respect to risk discovery and mitigation. It assesses classifiers within the Weka program based on execution measurements like as precision and exactness, and incorporates Sacking, J48, and Irregular Timberland. This uncovers abberations in their adequacy and calls into address the thought that cloud framework assurance ought to be accomplished by algorithmic consistency.

关键词： Technological innovation Machine learning algorithms Prevention and mitigation Cloud computing security Machine learning Classification algorithms Security Business

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Hybrid CNN-LSTM Machine Learning Algorithm for Driver Distraction Detection 2

Hybrid CNN-LSTM Machine Learning Algorithm for Driver Distra...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kothuri, Sri Raman Sujay, V. Sreedhar, Siva Satya Juyal, Prachi Jyothi Prasad, M. Bhuvaneshwari, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Machilipatnam India Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India Uttarakhand Dehradun India RMD Engineering College Department of ECE Tamil Nadu Chennai India Paavai Engineering College Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

Driver distraction is a critical safety concern on roadways, with potentially life-threatening consequences. The proposed system combines the spatial characteristic extraction capabilities of CNNs with the temporal modeling capabilities of LSTMs to effectively capture and analyze distractions in real-time. In the hybrid model, the CNN component processes visual input data, such as images or video frames, to extract spatial features that characterize driver behavior. These features are then fed into the LSTM component, which accounts for the temporal context and dependencies within the data. This approach promises to significantly enhance road safety by providing an intelligent system that can alert drivers and authorities to potential distractions, thereby reducing the risk of accidents. This study signifies a pivotal advancement in the progression of advanced driver assistance systems (ADAS) and driverless vehicles. The hybrid CNN-LSTM technique offers a promising route for safer and more reliable transportation systems with 93 % accuracy in detecting drivers' distractions. © 2024 IEEE.

关键词： activity recognition Advanced Driver Assistance Systems Distraction detection Long Short-Term Memory network object detection spatial features temporal

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